Method and system for identifying locations for placement of replenishment stations for vehicles

1. A method for identifying and displaying on a graphical user-interface one or more locations for placement of one or more replenishment stations for one or more vehicles, the method comprising:

receiving, by one or more processors, a historical demand data at a plurality of existing replenishment stations within a pre-defined area;

identifying, by the one or more processors, one or more point of interest locations within the pre-defined area based on a map data;

receiving, by the one or more processors, traffic information between a plurality of road intersections within the pre-defined area, from one or more sensors;

for each existing replenishment station of the plurality of existing replenishment stations, determining, by the one or more processors:

first distances to each of the existing replenishment stations,

second distances to each road intersection within the pre-defined area, and

a count of the one or more point of interest locations within a predetermined radius of the existing replenishment station;

creating, by the one or more processors:

a first replenishment prediction model based the historical demand data and the determined first distances,

a second replenishment prediction model based the historical demand data, traffic information between a plurality of road intersections within the pre-defined area, and the second distances, and

a third replenishment prediction model based on the historical demand data and the determined count of the one or more point of interest locations;

instructing, by one or more processors, the graphical user-interface to display a map of the pre-defined area;

receiving, by one or more processors, one or more user-selected locations within the pre-defined area;

determining, by the one or more processors, a first demand prediction, a second demand prediction, and a third demand prediction based on the first replenishment prediction model, the second replenishment prediction model, and the third replenishment prediction model, respectively, and the received one or more user-selected locations;

predicting, by the one or more processors, a replenishment demand at the one or more user-selected locations within the pre-defined area based on an aggregation of the first demand prediction, the second demand prediction, and the third demand prediction; and

identifying on the graphical user-interface, by the one or more processors, the one or more locations from the one or more user-selected locations for placement of the one or more replenishment stations based on the predicted replenishment demand at the one or more user-selected locations and a pre-defined threshold.

2. The method of claim 1 , further comprising transmitting, by the one or more processors, information pertaining to the identified one or more locations to a user-computing device.

3. The method of claim 2 , wherein the graphical user-interface is displayed on the user-computing device that includes a map, wherein one or more user interactive markers, corresponding to the one or more locations, are displayed on the map based on the transmitted information.

4. The method of claim 3 , wherein an input is received from a user of the user-computing device on the one or more user interactive markers.

5. The method of claim 2 , wherein the replenishment demand is displayed on a display screen of the user-computing device in a form of one or more graphical representations comprising a bar chart, a pie chart, a heat map, and/or a line chart.

6. The method of claim 1 , wherein the first replenishment prediction model, the second replenishment prediction model, and the third replenishment prediction model are created based on a canonical correlation analysis (CCA) technique.

7. The method of claim 1 , further comprising assigning, by the one or more processors, a weight to each of the first replenishment prediction model, the second replenishment prediction model, and the third replenishment prediction model, wherein the weight is indicative of an importance of each of the first replenishment prediction model, the second replenishment prediction model, and the third replenishment prediction model in predicting the replenishment demand.

8. The method of claim 1 , wherein the map data comprises coordinates of the one or more point of interest locations, wherein the one or more point of interest locations are classified into one or more categories, wherein the one or more categories comprise sustenance, education, transportation, financial, healthcare, entertainment, sports, gardens, place of worships, shops, and public buildings.

9. The method of claim 1 , wherein the replenishment stations correspond to a gas station or an electric vehicle charging station.

10. The method of claim 1 , wherein the one or more vehicles comprises a gasoline vehicle or an electric vehicle.

11. The method of claim 1 , wherein the historical demand data is received via a web service associated with each of the one or more replenishment stations.

12. The method of claim 1 , wherein the replenishment demand comprises a number of charge units consumed per hour at the one or more locations.

13. An application server to identify and display via a graphical user-interface one or more locations for placement of one or more replenishment stations for one or more vehicles, the application server comprising:

one or more processors configured to:

receive a historical demand data at a plurality of existing replenishment stations that are located within a pre-defined area;

identify one or more point of interest locations within the pre-defined area based on a map data;

receive traffic information between a plurality of road intersections within the pre-defined area, from one or more sensors;

for each existing replenishment station of the plurality of existing replenishment stations, determine:

first distances to each of the existing replenishment stations,

second distances to each road intersection within the pre-defined area, and

a count of the one or more point of interest locations within a predetermined radius of the existing replenishment station;

create:

a first replenishment prediction model based the historical demand data and the determined first distances,

a second replenishment prediction model based the historical demand data, traffic information between a plurality of road intersections within the pre-defined area, and the second distances, and

a third replenishment prediction model based on the historical demand data and the determined count of the one or more point of interest locations;

instruct the graphical user-interface to display a map of the pre-defined area;

receive one or more user-selected locations within the pre-defined area;

determine a first demand prediction, a second demand prediction, and a third demand prediction based on the first replenishment prediction model, the second replenishment prediction model, and the third replenishment prediction model, respectively, and the received one or more user-selected locations;

predict a replenishment demand at the one or more user-selected locations within the pre-defined area based on an aggregation of the first demand prediction, the second demand prediction, and the third demand prediction; and

identify on the graphical user-interface the one or more locations from the one or more user-selected locations for placement of the one or more replenishment stations based on the predicted replenishment demand at the one or more user-selected locations and a pre-defined threshold.

14. The application server of claim 13 , wherein the one or more processors are further configured to transmit information pertaining to the identified one or more locations to a user-computing device.

15. The application server of claim 14 , wherein the one or more processors are further configured to receive one or more input parameters from the user-computing device, wherein the one or more input parameters comprise the pre-defined area, the information pertaining to the one or more locations, and a time interval for which the replenishment demand at the one or more locations is predicted.

16. The application server of claim 14 , wherein the graphical user-interface is displayed on the user-computing device that includes a map, wherein one or more user interactive markers, corresponding to the one or more locations, are displayed on the map based on the transmitted information.

17. The application server of claim 13 , the first replenishment prediction model, the second replenishment prediction model, and the third replenishment prediction model are created based on a canonical correlation analysis (CCA) technique.

18. The application server of claim 13 , wherein the one or more point of interest locations are determined based on a number of visits by the one or more vehicles at each of the one or more point of interest locations.

19. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions for causing a computer comprising one or more processors to perform steps comprising:

receiving, by one or more processors, a historical demand data at a plurality of existing replenishment stations that are located within a pre-defined area;

identifying, by the one or more processors, one or more point of interest locations within the pre-defined area based on a map data;

receiving, by the one or more processors, traffic information between a plurality of road intersections within the pre-defined area, from one or more sensors;

for each existing replenishment station of the plurality of existing replenishment stations, determining, by the one or more processors:

first distances to each of the existing replenishment stations,

second distances to each road intersection within the pre-defined area, and

a count of the one or more point of interest locations within a predetermined radius of the existing replenishment station;

creating:

a first replenishment prediction model based the historical demand data and the determined first distances,

a second replenishment prediction model based the historical demand data, traffic information between a plurality of road intersections within the pre-defined area, and the second distances, and

a third replenishment prediction model based on the historical demand data and the determined count of the one or more point of interest locations;

instructing, by one or more processors, a graphical user-interface to display a map of the pre-defined area;

receiving, by one or more processors, one or more user-selected locations within the pre-defined area;

determining, by the one or more processors, a first demand prediction, a second demand prediction, and a third demand prediction based on the first replenishment prediction model, the second replenishment prediction model, and the third replenishment prediction model, respectively, and the received one or more user-selected locations;

predicting, by the one or more processors, a replenishment demand at the one or more user-selected of locations within the pre-defined area based on an aggregation of the first demand prediction, the second demand prediction, and the third demand prediction; and

identifying on the graphical user-interface, by the one or more processors, one or more locations from the one or more user-selected of locations for placement of one or more replenishment stations based on the predicted replenishment demand at the one or more user-selected of locations and a pre-defined threshold.